How Has Texas Survived the Loss of Kevin Durant?

Last week I received an e-mail from Thomas Hackett, a writer for the Austin Chronicle. Thomas wished to know why the Texas Longhorns were able to survive the loss of Kevin Durant. Hackett thought that it was because of the play of D.J. Augustin, but wanted to know if the numbers agreed with his assessment.

When I looked at the Texas numbers from 2006-07 and 2007-08, a different answer emerged. And since Hackett is a professional writer (and I am not), I think I will let him tell the story.

“Put Damion in!” someone yelled with a minute left in the first half and the University of Texas struggling against Colorado. No, the shout hadn’t come from Damion James himself, though it might have.

“I was begging to be put back in,” said the 6-foot-7-inch forward, benched for all but a couple of minutes of the half after picking up two quick fouls. “I knew when I got back in the game, I was going to bring it.”

And so he did. In the first eight minutes of the second half on Saturday, Texas erased a 9-point deficit to take a commanding 9-point lead. James scored only 6 of those points – it was hardly his best night of the season. Yet with his eight rebounds, he was the difference maker.

That comes as no surprise to Dr. David Berri, even though he has never seen James play. Berri is an economist and co-author of The Wages of Win, a mathematical analysis of basketball performance that turns a lot of conventional wisdom on its head. For example, individual scoring isn’t that big of a deal, according to Berri. The NBA’s Allen Iverson scores a lot, but he also misses a lot. What matters is overall efficiency, and for all his flair, Iverson is an incredibly inefficient player who doesn’t really help his team win.

I had called Berri to understand why the Longhorns are playing exactly as well this year (they’re 15-3) as they did last year, when they had the best college player in the country in Kevin Durant. I thought D.J. Augustin had to be the reason: To my eyes, the kid is clutch, averaging 20 points and six assists a game. But nope, that’s not it. According to a metric that Berri and his co-authors call a “win score,” the biggest reason the Longhorns have been able to pick up where Durant left off is James.

Last season Augustin had a win score of .149. So far this year, it’s .182 – an improvement but nothing compared to James’ .393, which is within spitting distance of Durant’s score of .403.

“It’s important to understand that when Kevin Durant is not there, somebody else is going to take those shots,” Berri explained. “The question is: Are they doing that and the other things that create wins efficiently?”

With most things we do in life, performance is difficult to accurately assess: The data can be limited, the criteria arbitrary, and the analysis subjective. That’s one appeal of sports: Objective measures reign supreme. If you don’t score more points than the other team, you lose. Even so, in a complex and fluid team sport like basketball, individual performance is still hard to separate from team performance. We know who won the game, but without meaningful statistical analysis, we just don’t know why. We don’t know who is responsible.

For his part, James doesn’t tout his stats and seems a bit surprised that some economist out in California would. Surprised but flattered.

“I don’t think about Damion James,” he says. “I just try to bring some energy and production when I’m out there – and help the Longhorns win. It’s a team thing.”

Hackett travelled around the various circuits of professional wrestling-that peculiar mixture of Olympic games and the burlesque, in which beefy athletes beat each other up in scripted bouts-determined to take its participants seriously. The result is an enjoyable and astute appraisal of a too easily maligned subculture. Hackett believes that wrestling, with its “blue collar” celebrity, convoluted sexuality, and faked reality, epitomizes something essential about American culture, although his attempts to discuss these theories with the subjects themselves often prove comically inconclusive. At one point, he tells a good natured young wrestler named Altar Boy Luke (who has just insisted that “wrestling is real,” unlike, say, “Star Trek”) that somewhere among the sport’s layers of fakery is a bit of truth, “and everybody is trying to figure out what that is.” “And the truth is,” the wrestler replies, “I’m an athlete and you’re an asshole!”

I think you have to admire a writer who inspires such a comment from a professional wrestler.

By the way, if anyone is interested, I could add a table detailing how each Texas player improved from last season. Please let me know in the comments.

UPDATE: The following table reports the analysis of the Texas Longhorns I provided to Thomas Hackett.

The table reports Win Score, Win Score per 48 minutes, and Win Score per minute for each player on the Longhorns in 2007-08 (after 13 games) and 2006-07. It’s important to note that the data has not been adjusted for position played. So the analysis only shows how a player has improved or regressed. It doesn’t show exactly how players compare to each other.

40 Responses to "How Has Texas Survived the Loss of Kevin Durant?"

[…] dberri wrote a fantastic post today on “How Has Texas Survived the Loss of Kevin Durant?”Here’s ONLY a quick extractThe NBA’s Allen Iverson scores a lot, but he also misses a lot. What matters is overall efficiency, and for all his flair, Iverson is an incredibly inefficient player who doesn’t really help his team win. … […]

Dave, I’d love to see you tackle college teams more often. But most of all, I hope you’ll repeat last year’s NBA Draft preview. How spot-on was your prediction that Al Hortford would provide a significant boost to the Atlanta Hawks?

Westy,
No. There is a shot clock that requires shots be taken. There is no rebound clock that requires that you rebound. If you employ players who don’t rebound well, you won’t get many rebounds.

Its important to remember, as has been stated many times, that rebounds are one of the most consistent aspects of a player’s performance. If it were the case that rebounds are simply going to happen, and your rebounds come at the expense of your teammates, then a player’s rebounds would go up and down depending on teammates. This is not what I see in the data.

While it’s possible to end an opponent’s possession without allowing them to take a shot, *most* of the time, a team gets off the shot, regardless of whether or not it’s a good shot. Denying scoring opportunities (preventing shots without fouling aka getting a turnover) is difficult and is the rare outcome of a possession. At least in the NBA, the variation between the best and worst teams in terms of the ability to get a shot off is rather small. The worst teams still manage to get a shot off on the vast majority of their possessions. Bad teams *still* manage to get off shots. They just tend to miss more of them than good teams. The competition doesn’t seem to be limiting shots so much as it dictates the quality of the shot.

What is the variation between the best and worst teams in rebounding the ball? Is it a much bigger difference than the variation between the best and worst teams in terms of the ability to get a shot off?

and waaaay off subject, but I noticed Rudy Gay is seemingly having a pretty good year. Whether or not he is by WP standards, I don’t know. Maybe you could look back at that Battier-Gay trade. Where would the Rockets be with Gay by your estimations?

What is the variation between the best and worst teams in rebounding the ball? Is it a much bigger difference than the variation between the best and worst teams in terms of the ability to get a shot off?
Exactly, antonio.

While there is no rebound clock, as has been stated, there is a shot clock and so teams shoot at a regular pace (whether it’s a good shot or not). A possession ends either with a made shot or a miss and a rebound. Thus, we can check the variance in teams’ defensive rebounds, which are also pretty regular.

As it turns out, defensive rebounding is less variable than shot attempts. So far this year, the top team rebounds 5.374 more defensive rebounds per game than the bottom team. On the other hand, the top team shoots 13.464 more shots than the bottom team.

This indicates to me that rebounding is something that pretty much is going to happen if shots are missed.

Westy,
But if that was true, then a player’s rebounds would fluctuate depending upon shots missed (and teammates, coaching, and everything else we credit rebounding for). This, though, is not in the data. Why can’t we just accept the idea that players who get rebounds are good at rebounding. And those who don’t, are not.

Antonio raises exactly the right question. The relationship between player and team rebounds can tell us what the individual player stats mean. Dave says “If it were the case that rebounds are simply going to happen, and your rebounds come at the expense of your teammates, then a player’s rebounds would go up and down depending on teammates. This is not what I see in the data.” But this is exactly what we see in the data, if we look in the right place. 82Games provides rebound totals by position for every team. This allows us to compare the rebound rate at each position and total rebound rate at the other 4 positions, and run a correlation. This is what you find for each position in 2006-07:

C: -0.65
PF: -0.57
PG: -0.62
SF: -0.49
SG: -0.15

The correlations are all negative, and with one exception, all large. What these results show is that a player’s Reb total has an enormous impact on the Reb total for other players on that team. When one position captures a lot of rebounds, the other 4 positions invariably under-perform. When one position has few rebounds, the other 4 positions over-perform. Obviously, this cannot be a coincidence. Usually, when a player appears to be a “great rebounder,” most of his rebounds above average come from his teammates rather than his opponents.

Individual players’ consistency from year-to-year — which Dave and Jason cite repeatedly — doesn’t show that players aren’t affected by teammates. Yes, Dennis Rodman had a lot of boards every year. But just as consistently, his teammates – over many years and several franchises – were “poor rebounders.” What you need to track is not Rodman, but Rodman’s impact on his teammates. And that was quite consistent – he took their rebounds.

I would just also add the below after Dave noted, “…then a player’s rebounds would fluctuate depending upon shots missed.”

While the available rebounds may indeed vary somewhat based on the number of opponents shots missed (a function of defense), the difference in shots missed by their opponent per game from the top team to the bottom team is 7.45 (slightly more than the difference in defensive rebounds from top to bottom). I would note that the most misses available per game belongs to Phoenix and the least to Utah, which points to a difference in pace more than anything to me.

Thus, the difference from team to team in rebounds is minimal. It would seem each team is employing a similar strategy to both maximize their defensive and rebounding presence balanced with their ability to score.

David, If I recall Jason checked for the correlation of a player’s rebound total this year and his total next year if he switched teams. And that correlation was still high. So the consistency year-to-year in a player’s rebounds isn’t an artifact of him simply having the same teammates. Discuss.

Quick comment on the variation in the data.
Here is the coefficeint of variation (the proper stat to look at when comparing variation across different variables) for various NBA statistics.
Offensive rebounds: 0.125
Defensive rebounds: 0.053
Ratio of Offensive Rebounds to Missed Shots: 0.095
Missed Field Goals: 0.053
Points per Field Goal Attempt [(PTS-FTM)/FGA]: 0.042
Field Goal Percentage: 0.042
This is for all teams from 1990-91 to 2006-07
What we see is there is a great deal of variation in offensive rebounds. And defensive rebounds vary as much as missed shots, and more than shooting efficiency. Apparently some teams are better at rebounding than others. I think that’s because some players are better at rebounding than others. And the teams that do better at rebounding, employ better rebounders.

It is the case that there is diminishing returns to rebounds. But I would argue, because players are so consistent in this stat, that the diminishing return effect is small. I have looked at the link between overall player productivity and teammate productivity, and again the effect is negative. And again, it is small.

The idea that great rebounder just coincidently get teammates who can’t rebound seems quite far fetched. Ben Wallace is a great rebounder, regardless of teammates. Eddy Curry can’t rebound well, regardless of teammates. It seems unlikely that Wallace always got teammates who let him take their rebounds. And that Curry always gets teammates who take his rebounds away.

DB:
If a player’s rebounds were largely independent from what his teammates do, then we wouldn’t see large negative correlations between a player’s (or position’s) rebound total and the rebound total for his teammates. But we do. The large negative correlations show that if a player averages 4 reb/game above average for his position, his team — on average — will be more like +1.5, and not anything close to +4. This is just stats 101, and I’m not sure why you didn’t address this.

You say “It seems unlikely that Wallace always got teammates who let him take their rebounds. And that Curry always gets teammates who take his rebounds away. ” I think I see where you went astray: players do not compete with their teammates to get rebounds, they compete with their opponents. There is simply no reason to think that every player on a team is trying to maximize his individual reb total, or that coaches ask them to. And Curry and Wallace are actually great counter-examples to your thesis. Here are their teams’ net reb/game the last 3 years:
Wallace
05-06 -0.34
06-07 0.00
07-08 +0.40
Curry
05-06 +2.82
06-07 +4.46
07-08 +0.67
Curry’s team has been better every year, usually a lot better, despite the huge advantage you think Wallace represents over Curry. And no, it’s not a coincidence at all: “great rebounders” almost always have “poor rebounders” (by your calculation) as teammates, and vice-versa. Wallace’s teammates may defer to him because he’s a slightly better rebounder, or because he likes to rebound, or for some other reason. conversely, Curry’s coaches elect to have other players concentrate on getting rebounds. We can speculate on the reasons, but the key point is this: neither Wallace’s good reb total nor Curry’s bad total has anything like a 1-to-1 impact on his team.

And you’ve made a critical error in your COV analysis: a team has many more shot attempts per game than rebounds, and more missed shots than defensive boards. So your numbers are all apples-to-oranges comparisons. Using your numbers, the std dev for points/FGA appears to be 3.36 (per game), vs. 1.39 for off rebs and 1.59 for def rebs. Even combining def and off Rebs, there’s clearly much more variation in shooting efficiency than rebounding ability.

Animal: As I already explained, a player’s year-to-year consistency only proves that players tend to have consistent roles with regard to rebounds, not that players don’t affect teammates. In the 1990s, Dennis Rodman would routinely grab over 500 rebounds per season above an average forward. But his teammates were almost always 300 to 500 rebounds below average. Coincidence? In Minn, Garnett was more than 300 rebounds above average, but his teammates were consistently about 300 rebounds below average. Another bad break. Look for yourself: you’ll find that this misfortune follows nearly every “great rebounder”.

I agree with this. I don’t think that means Rodmans or Garnetts teammates were bad rebounders, but simply because they have such a great rebounding player, they don’t attempt to rebound as much and are not going to rebound as much. It is not as if their teammates are always bad rebounders. They just simply don’t rebound because its not needed.
From a coaches point of view, if you have a great rebounder like one of them, why would you have your other players stay back and try to get defensive rebounds. It would be smarter to send them up the court to enhance your chance of getting fast break points. Obviously you don’t send all 4 players up, but you would send more than if you did not have a great rebounder

“It seems unlikely that Wallace always got teammates who let him take their rebounds. And that Curry always gets teammates who take his rebounds away.”

I disagree. I said this in the previous post, but if you have a great rebounder like Wallace, getting rebounds is not a priority for you. If you have a terrible rebounder like Curry, I am sure the players realize they need to concentrate more on rebounding to pick up the slack from him.

Also, it is not that they are taking Curry’s rebounds away, but simply that they know Curry won’t get many rebounds and make a more conscientious effort to rebound the ball. And teammates are not letting Wallace get their rebounds, but simply realize he is capable and they don’t need to get as many rebounds as if an average rebounder were there.

Why can’t we just accept the idea that players who get a lot of rebounds are the most of the time (with the Rodmans and Kidds of the world exceptions of course) whose opponent coincidentially also get a lot of rebounds?

But, the real question is, how do you punish your team by all the off. rebounds the opponent team get, and how do you distribute this punishment? – How do you punish your team by not to avoid the opponent get def. rebounds?

All the problem starts when team possessions are totally assigned to individual players. When in the relationship Poss Employed/Poss Acquired between the FGMissed/Reb, the players called to grab off. rebounds are taken out of context as employers of the team off. possession (they just come in as ocassional saviors, but not obligued to avoid the opp.’ def. reb.); and players called to avoid off. rebounds as employers when they allow the opponent to get a second poss. chance.

It’s not that rebounds are statistically and possessionally overrated, Rebounding is not properlly rated (neither against the opponent, nor the teammates).

As best I can tell, Kidd is no exception to the pattern that “great rebounders” (whom we should really call “players with high reb totals”) have teammates who are “poor rebounders.” Here is Kidd’s rebounds-above-average for past ten seasons — what WP credits him with — alongside his team’s net rebounds (own reb minus opp reb):

Notice that the entire team is worse than Kidd every single year, usually by a lot. Not once in ten years has Kidd played with teammates who were so much as average rebounders. Overall, Kidd’s teammates have been about 3 rebounds per game below average. Quite the string of bad luck.

* *

DB: You mentioned the SD for team DefReb above. So far this season, it’s 1.54. However, the SD for def Reb% is just .019, which means just 0.8 def Reb per game (assuming 42 opportunities). This would seem to suggest that opportunities — i.e. opponent FG% — does have a significant impact on rebound totals. It also shows that the actual range of rebounding skill at the team level is pretty small.

Dan, the numbers you reported for the correlation, what was that statistic? Since you are reporting negative numbers, is it the slope of the observed trend? It’s important to note that a negative slope can (and does) exist in a case of diminishing returns. In a game with a finite number of opportunities for rebounds, the negative slope across the league should be expected. But it’s a mistake to confuse a negative slope with the statistical significance of the result or the degree with which the independent variable explains the dependent variable. It would help greatly if you defined your dimensions when reporting a number.

Also, your comparison of standard deviations is a bit confused. I’m not sure you’re comparing the statistics correctly. Comparing standard deviations directly between numbers with highly different mean values does not tell you anything about how variable one dataset is with regards to the other. To gauge this, you need to look at the ratio of the standard deviation to the mean value. This avoids the “apples to oranges” problem. (They are, after all, both round fruit that are seasonally best in winter, and not all that dissimilar nor difficult to compare if you do it correctly.)

Jason:
The correlation was for these two variables: Reb%(pos) and Reb%(other 4 postions), where Reb% = Reb(pos)/(Reb(tm) + Reb(opp)). But I also ran it for straight rebounds (position vs. other positions), without correcting for rebound opportunities, and the results are similar.

If a player who is above average in rebounds is mainly taking rebounds from his opponents, as WP assumes, then there should be little or no negative slope here. In that scenario, the number of rebounds grabbed by the centers should have no impact on the rebounds gained by the other 4 positions. But that is not what we see in the data.

There was one error in my figures: the correlation for SG should be -0.46, in line with the other positions. DB has the spreadsheet if you’re interested in looking at it.

On COV and SDs, I’m afraid you’re mistaken. To create a level playing field, you need a common currency. My SDs are all stated in terms of the impact on points per game, which is what you want. If we want to say that NBA teams vary more in skill X than in skill Y, what we mean — or should mean — is that the differences in skill X have more impact on (i.e. explain more) actual wins and losses. COV, in contrast, doesn’t tell us anything about points/wins. The COV on TOs, for example, could be very high, but not translate into a lot of points because the denominator is small (just an example, haven’t calculated the COV for TOs).

Since wins and losses are the foundation of WP, I’m sure you’ll agree.

“David,” you did not address my question: the negative numbers you presented. What were they? Were they the slope of the trendline? It still does not look like you are familiar with the difference between this and the statistical significant nor the degree with which variables covary. At least as you’ve presented it, it does not sound like you are familiar with these differences. I could be wrong, but you are clearly leaving something out and as such, your conclusions aren’t justified. It sounds much more like a confused translation of someone else’s explanation. Perhaps you should clear this up.

Again: when you say “the correlation for SG should be -0.46″ what is that number? What are the units? What is the statistical strength of that number on the dependent variable? How tight is the correlation? I cannot tell anything about that from what you’ve presented. Your presentation is incomplete. Again, it reads like a poor translation of someone else’s thoughts. Is it?

Jason: the values are Pearson correlation coefficients (“r”). I’m surprised you’re unfamiliar with them. You can find a quick summary here: http://en.wikipedia.org/wiki/Correlation. Basically, they measure the strength of the linear relationship between two variables, with a value between -1 and 1. These large negative values mean that the more rebounds a team gets from its center, the fewer rebounds it gets from its other 4 positions (on average, of course). And the same is true for every position.

But if you’re not comfortable with this kind of statistical analysis, just take a look at the team totals for some “great rebounders.” Invariably, the team’s net rebounds is smaller than the rebounder’s own net rebounds above his position average. This is true for Kidd, Garnett, Rodman, etc. That means the rebounders’ teammates must be below average for their positions.

Here’s a test: see if you can find even 5 great rebounders — say, 3+ Reb48 above average — whose teammates were average (or better) rebounders over the course of his career. If a player’s rebounds are largely independent of his teammates, almost half of the good rebounders should fit this description. So it should only take you a few minutes. But you’ll find that it’s very hard to find them.

Just so people know…
David is Guy Washington (who is really Guy Molyneux). I would love to hear about the thought process that leads someone to post under so many different aliases. So perhaps Guy can explain this at some point.

As I have tried to explain to Guy via private e-mail, he does not understand “coefficient of variation.” Here is a fairly standard definition (which I took from Investopedia).

The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other.

The coefficient of variation of ORB and shooting efficiency, which I posted earlier, clearly shows that ORB vary more across teams than shooting efficienty. It is the case that some teams are better at rebounding than others. So players can increase rebounds without simply taking these from teammates.

That being said, there is diminishing returns in the NBA. The Wages of Wins makes this very clear, and we provide a much better test than one year of correlations.

In sum, Guy — under his latest alias — is saying nothing that the Wages of Wins hasn’t said before.

Of course, knowing Guy, he will keep posting until we all give up and he gets the last word. Hopefully his last word will consist of telling us that he now understands “coefficient of variation.” Oh, and also why he needs so many aliases.

I was thinking about why Guy Molyneux would invent an on-line persona “Guy Washington” and have that semi-pseudononymous handle morph into “David.” It occured to me that he might do it to give the appearance of a bunch of people espousing the same view, with repetition seemingly making the argument more convincing. Mr. Molyneux, if you just presented evidence compellingly in support of your view under one name that would be sufficient. Half-formed arguments under “David,” “Harold A,” and “Guy” are less convincing.

“David/Guy”, it isn’t that I’m unfamiliar with the coefficient. I simply try not to read minds and you did not declare what your figures were. I try not to presume that a undeclared number is a result of a particular analysis when someone does not tell me what the analysis is. I try not to presume what test someone performed when they didn’t tell me. It would have been better if had simply said, from the get go, what test you had used such that it was transparent; much better than the tack you took, which was to ignore the request once, then reply a second time with mock surprise.

Numbers, especial dimensionless numbers, can be many, many things. Making yourself clear by explaining what test was performed is important, Guy.

I had asked what the statistic you used was the first time. You did not answer it. If you want to assume that it’s because I don’t know the statistical methodology, you’re free to do so, but in so, you’re going to be wrong. Now instead of answering the first time (e.g. “Jason, the test I did was for the Pearson correlation coefficient…) you went on to discuss something else, then to feign surprise because I didn’t intuitively discern what test you’d performed. I asked again because you (yes, you, Guy/David) did not make it clear what test you did. It is not transparent from a series of numbers. Your retort, trying to make it an attack on my abilities and knowledge when you know that you didn’t actually provide infomation, is a ruse that indicates only that when pressed to answer a question, you resort to being a jerk.

Are you a jerk, Guy?

Now, that said, it’s rather interesting that there’s a negative correlation. Again, what’s the fit of the model? What’s the statistical *significance* of the negative correlation? I’ve asked this before. Why did you not answer before?

Wow (or is that “WOW”?) — I appear to have entered a bit of a war zone here. Jason: I thought I was being clear enough in saying I had measured the correlation between two variables, but I guess I wasn’t. And apologies if I mistook your inquiry for a lack of knowledge on your part. In any case, I’ll just send you the data (which DB also has), and you can decide for yourself if the results are significant.

And don’t forget to look for some great rebounders whose teammates were not “poor rebounders.” :>)

I honestly dont see anywhere where Guy/David is being a jerk, but Jason, in most of your posts I can certainly say you come off as stuck up and obnoxious. So how bout you leave the immature name calling out of it. I know you are “so offended” that he did not originally answer your question in his response but in his second response, but take it easy there. Your attitude is certainly not conducive to a productive conversation.

And Dberri, why is it necessary to let his name be known. If for whatever reason he wanted his name to be kept private, you should respect his privacy. I can not believe how defensive and immature you and Jason get just because somebody questions the model. To call him a jerk and let his name be known when he has tried to keep it secret to not let it be known is childish and pathetic.

antonio,
There is a history with Guy in this forum (and in other places like Sabernomics and The Sports Economist) that you are not aware of. If you understood that history, you would understand our reaction.